miχpods - TAO

• eulerian 0-40m control volume

• focus on Jq

• use model chl / double exponentials

%load_ext watermark

import os

import cf_xarray
import dask
import dcpy
import distributed
import flox.xarray
import hvplot.xarray
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
import dcpy
import glob


import pump
from pump import mixpods

dask.config.set({"array.slicing.split_large_chunks": False})
mpl.rcParams["figure.dpi"] = 140
xr.set_options(keep_attrs=True)

gcmdir = "/glade/campaign/cgd/oce/people/bachman/TPOS_1_20_20_year/OUTPUT/"  # MITgcm output directory
stationdirname = gcmdir

%watermark -iv

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask_jobqueue/core.py:20: FutureWarning: tmpfile is deprecated and will be removed in a future release. Please use dask.utils.tmpfile instead.
  from distributed.utils import tmpfile

matplotlib : 3.6.3
dask       : 2023.1.0
distributed: 2023.1.0
flox       : 0.6.7
hvplot     : 0.8.2
json       : 2.0.9
numpy      : 1.23.5
xarray     : 2023.1.0
pump       : 0.1
cf_xarray  : 0.7.7
dcpy       : 0.1.dev385+g121534c
sys        : 3.10.8 | packaged by conda-forge | (main, Nov 22 2022, 08:26:04) [GCC 10.4.0]

import dask_jobqueue

if "client" in locals():
    client.close()
    del client
if "cluster" in locals():
    cluster.close()

cluster = dask_jobqueue.PBSCluster(
    cores=4,  # The number of cores you want
    memory="23GB",  # Amount of memory
    processes=1,  # How many processes
    queue="casper",  # The type of queue to utilize (/glade/u/apps/dav/opt/usr/bin/execcasper)
    local_directory="/local_scratch/pbs.$PBS_JOBID",
    log_directory="/glade/scratch/dcherian/dask/",  # Use your local directory
    resource_spec="select=1:ncpus=4:mem=23GB",  # Specify resources
    project="ncgd0011",  # Input your project ID here
    walltime="02:00:00",  # Amount of wall time
    interface="ib0",  # Interface to use
)
cluster.adapt(minimum_jobs=2, maximum_jobs=8)
# cluster.scale(4)
client = distributed.Client(cluster)
client

Client

Client-3953f65f-a36d-11ed-91ee-3cecef1acbfa

Connection method: Cluster object	Cluster type: dask_jobqueue.PBSCluster
Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/proxy/8787/status

Launch dashboard in JupyterLab

Cluster Info

PBSCluster

e60dbc56

Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/proxy/8787/status	Workers: 0
Total threads: 0	Total memory: 0 B

Scheduler Info

Scheduler

Scheduler-c2c360ba-a15d-4c06-940f-fe48464e1bf0

Comm: tcp://10.12.206.63:40066	Workers: 0
Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/proxy/8787/status	Total threads: 0
Started: Just now	Total memory: 0 B

Workers

tao_gridded = mixpods.load_tao()
tao_gridded["Ih"] = 0.45 * tao_gridded.swnet * np.exp(-0.04 * np.abs(tao_gridded.mldT))

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:256: UserWarning: The specified Dask chunks separate the stored chunks along dimension "depth" starting at index 42. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:256: UserWarning: The specified Dask chunks separate the stored chunks along dimension "time" starting at index 199726. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:256: UserWarning: The specified Dask chunks separate the stored chunks along dimension "longitude" starting at index 2. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(

Moum et al (2013) Climatologies

• The published .nc file seems “over cleaned” with mask ε < 3e-6 or so

tropflux = xr.open_mfdataset(
    sorted(glob.glob("/glade/work/dcherian/datasets/tropflux/**/*.nc")),
    engine="netcdf4",
    parallel=True,
)

tropflux_0140 = tropflux.interp(latitude=0, longitude=360 - 140).load()
tropflux_clim = tropflux_0140.groupby("time.month").mean()

Monthly climatology, Vertical mean

Didn’t work with published nc file

clim = xr.Dataset()
clim["netflux"] = (
    tropflux_0140.netflux.sel(time=slice("2005", "2011")).groupby("time.month").mean()
)
clim["Ih"] = tao_gridded.Ih.sel(time=slice("2005", "2011")).groupby("time.month").mean()
clim["Jq"] = (
    tao_gridded.Jq.sel(time=slice("2005", "2011"), depthchi=slice(-60, -20))
    .groupby("time.month")
    .mean(["depthchi", "time"])
)

(-1 * clim.Jq).plot(ylim=(0, 200))
(clim.netflux - clim.Ih).plot(ylim=(0, 200))

[<matplotlib.lines.Line2D>]

Try with .mat file

cchdo = dcpy.oceans.read_cchdo_chipod_file(
    "/glade/u/home/dcherian/datasets/microstructure/osu/chipod/tao/chipods_0_140W.nc"
)
cchdo

<xarray.Dataset>
Dimensions:     (depth: 7, time: 107588)
Coordinates:
    timeSeries  (depth) float64 ...
  * time        (time) datetime64[ns] 2005-09-23T04:30:00 ... 2017-12-31T23:2...
    lat         (depth) float64 dask.array<chunksize=(7,), meta=np.ndarray>
    lon         (depth) float64 dask.array<chunksize=(7,), meta=np.ndarray>
  * depth       (depth) float64 29.0 39.0 49.0 59.0 69.0 89.0 119.0
Data variables:
    T           (depth, time) float64 dask.array<chunksize=(7, 10000), meta=np.ndarray>
    dTdz        (depth, time) float64 dask.array<chunksize=(7, 10000), meta=np.ndarray>
    N2          (depth, time) float64 dask.array<chunksize=(7, 10000), meta=np.ndarray>
    KT          (depth, time) float64 dask.array<chunksize=(7, 10000), meta=np.ndarray>
    chi         (depth, time) float64 dask.array<chunksize=(7, 10000), meta=np.ndarray>
    eps         (depth, time) float64 dask.array<chunksize=(7, 10000), meta=np.ndarray>
    Jq          (depth, time) float64 dask.array<chunksize=(7, 10000), meta=np.ndarray>
Attributes: (12/68)
    ncei_template_version:           NCEI_NetCDF_TimeSeries_Orthogonal_Templa...
    featureType:                     timeSeries
    title:                           Turbulence quantities measured by chipod...
    summary:                         Turbulence data in upper w m / all good ...
    keywords:                        sea_water_temperature, square_of_brunt_v...
    Conventions:                     CF-1.6, ACDD-1.3
    ...                              ...
    reference8:                      Moum, J.N., Ocean speed and turbulence m...
    reference9:                      Warner, S.J., J. Becherer, K. Pujiana, E...
    reference10:                     Moum, J.N., K. Pujiana, R-C. Lien and W....
    reference11:                     Becherer, J. and J.N. Moum, An efficient...
    reference12:                     Moulin, A.J., J.N. Moum and E.L. Shroyer...
    reference13:                     Thakur, R., E.L. Shroyer, R. Govindaraja...

xarray.Dataset

• Dimensions:
  • depth: 7
  • time: 107588
• Coordinates: (5)
  • timeSeries
    (depth)
    float64
    ...

    long_name :

    depths of Time series of T, dTdz, Nsqr, chi, epsilon, and Jq along a mooring line at 0-140W

    cf_role :

    timeseries_id

    [7 values with dtype=float64]

  • time
    (time)
    datetime64[ns]
    2005-09-23T04:30:00 ... 2017-12-...

    long_name :

    Time, UTC

    standard_name :

    time

    axis :

    T

    array(['2005-09-23T04:30:00.000000000', '2005-09-23T05:29:59.999996672',
           '2005-09-23T06:30:00.000003328', ..., '2017-12-31T21:30:00.000003328',
           '2017-12-31T22:30:00.000000000', '2017-12-31T23:29:59.999996672'],
          dtype='datetime64[ns]')

  • lat
    (depth)
    float64
    dask.array<chunksize=(7,), meta=np.ndarray>

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    valid_min :

    0.0

    valid_max :

    0.0

    Array Chunk Bytes 56 B 56 B Shape (7,) (7,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lon
    (depth)
    float64
    dask.array<chunksize=(7,), meta=np.ndarray>

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    axis :

    X

    valid_min :

    -140.0

    valid_max :

    -140.0

    Array Chunk Bytes 56 B 56 B Shape (7,) (7,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • depth
    (depth)
    float64
    29.0 39.0 49.0 59.0 69.0 89.0 119.0

    long_name :

    depths of sensors along the mooring line

    standard_name :

    depth

    units :

    m

    axis :

    Z

    positive :

    down

    valid_min :

    29.0

    valid_max :

    119.0

    array([ 29.,  39.,  49.,  59.,  69.,  89., 119.])

• Data variables: (7)
  • T
    (depth, time)
    float64
    dask.array<chunksize=(7, 10000), meta=np.ndarray>

    long_name :

    sea water temperature

    standard_name :

    sea_water_temperature

    units :

    C

    FillValue :

    -9999

    valid_min :

    283.15

    valid_max :

    308.15

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    measured by fast thermistor FP07

    references :

    Moum, J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 5.75 MiB 546.88 kiB Shape (7, 107588) (7, 10000) Dask graph 11 chunks in 4 graph layers Data type float64 numpy.ndarray

  • dTdz
    (depth, time)
    float64
    dask.array<chunksize=(7, 10000), meta=np.ndarray>

    long_name :

    vertical temperature gradient

    units :

    K m-1

    FillValue :

    -9999

    valid_min :

    4.9999999999999996e-06

    valid_max :

    0.001

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    references :

    Moum, J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 5.75 MiB 546.88 kiB Shape (7, 107588) (7, 10000) Dask graph 11 chunks in 3 graph layers Data type float64 numpy.ndarray

  • N2
    (depth, time)
    float64
    dask.array<chunksize=(7, 10000), meta=np.ndarray>

    long_name :

    squared buoyancy frequency

    standard_name :

    square_of_brunt_vaisala_frequency_in_sea_water

    units :

    s-2

    FillValue :

    -9999

    valid_min :

    1e-10

    valid_max :

    0.001

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    references :

    Moum, J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 5.75 MiB 546.88 kiB Shape (7, 107588) (7, 10000) Dask graph 11 chunks in 3 graph layers Data type float64 numpy.ndarray

  • KT
    (depth, time)
    float64
    dask.array<chunksize=(7, 10000), meta=np.ndarray>

    long_name :

    turbulent diffusivity of temperature

    standard_name :

    ocean_vertical_heat_diffusivity

    units :

    m2 s-1

    FillValue :

    -9999

    valid_min :

    1e-10

    valid_max :

    0.1

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    inferred from fast thermistor spectral scaling

    references :

    Moum, J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 5.75 MiB 546.88 kiB Shape (7, 107588) (7, 10000) Dask graph 11 chunks in 3 graph layers Data type float64 numpy.ndarray

  • chi
    (depth, time)
    float64
    dask.array<chunksize=(7, 10000), meta=np.ndarray>

    long_name :

    dissipation of temperature variance

    units :

    K2 s-1

    FillValue :

    -9999

    valid_min :

    1.0000000000000001e-11

    valid_max :

    0.0001

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    inferred from fast thermistor spectral scaling

    references :

    Moum J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 5.75 MiB 546.88 kiB Shape (7, 107588) (7, 10000) Dask graph 11 chunks in 3 graph layers Data type float64 numpy.ndarray

  • eps
    (depth, time)
    float64
    dask.array<chunksize=(7, 10000), meta=np.ndarray>

    long_name :

    turbulence dissipation rate

    standard_name :

    specific_turbulent_kinetic_energy_dissipation_in_sea_water

    ncei_name :

    turbulent kinetic energy (TKE) dissipation rate

    units :

    W kg-1

    FillValue :

    -9999

    valid_min :

    1e-12

    valid_max :

    9.999999999999999e-06

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    inferred from fast thermistor spectral scaling

    references :

    Moum J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 5.75 MiB 546.88 kiB Shape (7, 107588) (7, 10000) Dask graph 11 chunks in 3 graph layers Data type float64 numpy.ndarray

  • Jq
    (depth, time)
    float64
    dask.array<chunksize=(7, 10000), meta=np.ndarray>

    long_name :

    turbulent heat flux

    units :

    W m-2

    FillValue :

    -9999

    valid_min :

    -800.0

    valid_max :

    0.0

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    inferred from fast thermistor spectral scaling

    references :

    Moum J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 5.75 MiB 546.88 kiB Shape (7, 107588) (7, 10000) Dask graph 11 chunks in 3 graph layers Data type float64 numpy.ndarray

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex([          '2005-09-23 04:30:00',
                   '2005-09-23 05:29:59.999996672',
                   '2005-09-23 06:30:00.000003328',
                             '2005-09-23 07:30:00',
                   '2005-09-23 08:29:59.999996672',
                   '2005-09-23 09:30:00.000003328',
                             '2005-09-23 10:30:00',
                   '2005-09-23 11:29:59.999996672',
                   '2005-09-23 12:30:00.000003328',
                             '2005-09-23 13:30:00',
                   ...
                   '2017-12-31 14:29:59.999996672',
                   '2017-12-31 15:30:00.000003328',
                             '2017-12-31 16:30:00',
                   '2017-12-31 17:29:59.999996672',
                   '2017-12-31 18:30:00.000003328',
                             '2017-12-31 19:30:00',
                   '2017-12-31 20:29:59.999996672',
                   '2017-12-31 21:30:00.000003328',
                             '2017-12-31 22:30:00',
                   '2017-12-31 23:29:59.999996672'],
                  dtype='datetime64[ns]', name='time', length=107588, freq=None))

  • depth
    PandasIndex

    PandasIndex(Float64Index([29.0, 39.0, 49.0, 59.0, 69.0, 89.0, 119.0], dtype='float64', name='depth'))

• Attributes: (68)

  ncei_template_version :

  NCEI_NetCDF_TimeSeries_Orthogonal_Template_v2.0

  featureType :

  timeSeries

  title :

  Turbulence quantities measured by chipods on a TAO mooring line at 0-140W from 23-Sep-2005 to 31-Dec-2017

  summary :

  Turbulence data in upper w m / all good data between 2005 and 2016 - to be updated as analysis progresses. Data gaps are due to many reasons including lost moorings.

  keywords :

  sea_water_temperature, square_of_brunt_vaisala_frequency_in_sea_water, ocean_vertical_heat_diffusivity, specific_turbulent_kinetic_energy_dissipation_in_sea_water

  Conventions :

  CF-1.6, ACDD-1.3

  naming_authority :

  edu.oregonstate.coas.mixing

  history :

  2019-07-08 10:19:13 -ncatted

  source :

  fast thermistors FP07

  processing_level :

  3: Variables mapped on uniform space-time grid from higher resolution

  comment :

  See reference1 through reference13

  acknowledgment :

  Funding from the National Science Foundation ... with in-kind support from NOAA in the form of shipping, handling, deployment, recovery

  license :

  no restrictions

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table v49

  date_created :

  2019-07-08T10:19:15415Z

  creator_name :

  Jim Moum / Sally Warner / Aurelie Moulin / Oregon State University Ocean Mixing Group

  creator_url :

  http://mixing.coas.oregonstate.edu

  institution :

  Ocean Mixing Group, College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, USA

  project :

  TAO, PIRATA

  publisher_name :

  Jim Moum / Sally Warner / Aurelie Moulin / Oregon State University Ocean Mixing Group

  publisher_url :

  http://mixing.coas.oregonstate.edu

  geospatial_bounds :

  POINT (0 -140)

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_bounds_vertical_crs :

  EPSG:5831

  geospatial_lat_min :

  0.0

  geospatial_lat_max :

  0.0

  geospatial_lon_min :

  -140.0

  geospatial_lon_max :

  -140.0

  geospatial_vertical_min :

  29.0

  geospatial_vertical_max :

  119.0

  geospatial_vertical_positive :

  down

  time_coverage_start :

  2005-09-23T04:30:00000Z

  time_coverage_end :

  2017-12-31T23:30:00000Z

  time_coverage_duration :

  P12Y3M7DT19H0M00S

  time_coverage_resolution :

  PT1H

  sea_name :

  Equatorial Pacific Ocean

  creator_type :

  group

  creator_institution :

  College of Earth, Ocean, and Atmospheric Science, Oregon State University

  publisher_type :

  group

  publisher_institution :

  College of Earth, Ocean, and Atmospheric Science, Oregon State University

  contributor_name :

  Jim Moum, Sally Warner, Sasha Perlin, Aurelie Moulin

  contributor_role :

  Processed and/or archived data

  geospatial_lat_units :

  degrees_north

  geospatial_lon_units :

  degrees_west

  geospatial_vertical_units :

  m

  date_modified :

  2019-07-08T10:19:27096Z

  date_issued :

  2019-07-08T10:19:27416Z

  date_metadata_modified :

  2019-07-08T10:19:27663Z

  product_version :

  v2.0

  keywords_vocabulary :

  CF Standard Names

  platform :

  TAO mooring

  platform_vocabulary :

  NCEI Ocean Archive System Database

  instrument :

  chipod

  instrument_vocabulary :

  instrument designed by the Ocean Mixing Group at Oregon State University to measure turbulence quantities (http://mixing.coas.oregonstate.edu)

  cdm_data_type :

  Station

  reference1 :

  Moum, J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

  reference2 :

  Zhang, Y. and J.N. Moum, Inertial-convective subrange estimates of thermal variance dissipation rate from moored temperature measurements, J. Atmos. and Oceanic Tech., 27, 1950-1959, 2010. pdf: http://mixing.coas.oregonstate.edu/papers/zhang&moum2010.pdf

  reference3 :

  Moum, J.N., J.D. Nash and W.D. Smyth, Narrowband high-frequency oscillations at the equator. Part I: Interpretation as shear instabilities, J. Phys. Oceanogr, 41, 397-411, 2011. pdf: http://mixing.coas.oregonstate.edu/papers/2010JPO4450.pdf

  reference4 :

  Smyth, W.D., J.N. Moum and J.D. Nash, Narrowband high-frequency oscillations at the equator. Part II: Properties of shear instabilities, J. Phys. Oceanogr, 41, 412-428, 2011. pdf: http://mixing.coas.oregonstate.edu/papers/2010JPO4451.pdf

  reference5 :

  Perlin, A. and J.N. Moum, Comparison of Thermal Variance Dissipation Rates from Moored and Profiling Instruments at the Equator, J. Atmos. Oceanic Tech., 29, 1347-1362, 2012. pdf: http://mixing.coas.oregonstate.edu/papers/chipod_cham_chi_2012.pdf

  reference6 :

  Moum, J.N., A. Perlin, J.D. Nash and M.J. McPhaden, Seasonal sea surface cooling in the equatorial Pacific cold tongue controlled by ocean mixing. Nature, 500, 64-67. doi:10.1038/nature12363, 2013. pdf: https://www.nature.com/articles/nature12363

  reference7 :

  Smyth, W.D. and J.N. Moum, Marginal instability and deep cycle mixing in the eastern equatorial Pacific Ocean. Geophys Res. Lett., 40, 6181-6185, DOI: 10.1002/2013GL058403, 2013. pdf: http://mixing.coas.oregonstate.edu/papers/SmythMoum13_GRL_MI.pdf

  reference8 :

  Moum, J.N., Ocean speed and turbulence measurements using pitot-static tubes on moorings, J. Atmos. Oceanic Technol., 32, 1400-1413, doi:10.1175/JTECH-D-14-00158.1, 2015. pdf: https://journals.ametsoc.org/doi/full/10.1175/JTECH-D-14-00158.1

  reference9 :

  Warner, S.J., J. Becherer, K. Pujiana, E.L. Shroyer, M. Ravichandran, V.P. Thangaprakash and J.N. Moum, Monsoon mixing cycles in the Bay of Bengal: A year-long subsurface mixing record. Oceanography, 29(2):158?169, http://dx.doi.org/10.5670/oceanog.2016.4, 2016. pdf: http://www.tos.org/oceanography/article/monsoon-mixing-cycles-in-the-bay-of-bengal-a-year-long-subsurface-mixing-re

  reference10 :

  Moum, J.N., K. Pujiana, R-C. Lien and W.D. Smyth, Ocean feedback to pulses of the Madden-Julian Oscillation in the equatorial Indian Ocean. Nature Commun. 7, 13203 doi: 10.1038/ncomms13203, 2016. pdf: https://www.nature.com/articles/ncomms13203

  reference11 :

  Becherer, J. and J.N. Moum, An efficient scheme for onboard reduction of moored chipod data. J. Atmos. Oceanic Technol., 34, 2533-2546, doi:10.1175/JTECH-D-17-0118.1, 2017. pdf: https://journals.ametsoc.org/doi/full/10.1175/JTECH-D-17-0118.1

  reference12 :

  Moulin, A.J., J.N. Moum and E.L. Shroyer, Evolution of turbulence in the diurnal warm layer. J. Phys. Oceanogr., 48, 383-396, 2018. pdf: https://journals.ametsoc.org/doi/full/10.1175/JPO-D-17-0170.1

  reference13 :

  Thakur, R., E.L. Shroyer, R. Govindarajan, J.T. Farrar, R.A. Weller and J.N. Moum, Seasonality and buoyancy suppression of turbulence in the Bay of Bengal, Geophys. Res. Lett., https://doi.org/10.1029/2018GL081577, 2019. pdf: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018GL081577

def read_chipod_mat_file(fname):
    import h5py
    import pandas as pd

    file = h5py.File(
        "/glade/u/home/dcherian/work/pump/datasets/microstructure/chipods_0_140W_hourly.mat",
        mode="r",
    )

    chipod = xr.Dataset()

    chipod["depth"] = ("depth", file["chr"]["depth"][:].squeeze())
    chipod["time"] = ("time", file["chr"]["time"][:].squeeze())
    chipod["time"] = pd.to_datetime(chipod.time.data - 719529, unit="D")
    for var in ["Jq", "Kt", "N2", "T", "chi", "dTdz", "eps"]:
        chipod[var] = xr.DataArray(file["chr"][var][:, :], dims=("time", "depth"))
    return chipod


mat = read_chipod_mat_file(
    os.path.expanduser("~/work/pump/datasets/microstructure/chipods_0_140W_hourly.mat")
)

mat.Jq.where(np.abs(mat.Jq) < 10000).groupby("time.year").mean().sel(
    year=slice(2005, 2011)
).hvplot.line(
    by="year", y="depth", flip_yaxis=True
)  # .opts(legend_cols=1)

Compare

h1 = (
    cchdo.Jq.reset_coords(drop=True)
    .resample(time="M")
    .mean()
    .hvplot.quadmesh(
        cmap="greys",
        flip_yaxis=True,
        frame_width=1200,
        frame_height=200,
        clim=(40, -100),
    )
)
h2 = (
    mat.Jq.resample(time="M")
    .mean()
    .hvplot.quadmesh(
        cmap="greys",
        flip_yaxis=True,
        frame_width=1200,
        frame_height=200,
        clim=(40, -100),
    )
)

(h2.opts(title=".mat") + h1.opts(title="CCHDO")).cols(1)

h = (
    (
        np.abs(cchdo.Jq.resample(time="D").mean())
        .reset_coords(drop=True)
        .hvplot.line(by="depth", x="time")
        .opts(frame_width=1200)
        + np.abs(mat.Jq.resample(time="D").mean())
        .reset_coords(drop=True)
        .hvplot.line(by="depth", x="time")
        .opts(frame_width=1200)
    )
    .opts(shared_axes=True)
    .cols(1)
)
h

def plot_monthly_clim(ds, **kwargs):
    return np.abs(
        ds.Jq.sel(time=slice("2005", "2011-02"))
        .sel(depth=slice(20, 60))
        .mean("depth")
        .groupby("time.month")
        .mean()
    ).hvplot.line(**kwargs)


(
    plot_monthly_clim(mat, label=".mat")
    * plot_monthly_clim(mat.where(np.abs(mat.Jq) < 20000), label=".mat, Jq < 20000")
    * plot_monthly_clim(mat.where(np.abs(mat.Jq) < 10000), label=".mat, Jq < 10000")
    * plot_monthly_clim(mat.where(np.abs(mat.Jq) < 3000), label=".mat, Jq < 3000")
    * plot_monthly_clim(mat.where(mat.eps < 5e-4), label=".mat, ε < 5e-4")
    * plot_monthly_clim(mat.where(mat.eps < 1e-5), label=".mat, ε < 1e-5")
    * plot_monthly_clim(mat.where(mat.eps < 3e-6), label=".mat, ε < 3e-6")
    * plot_monthly_clim(cchdo, label="CCHDO", color="k")
    * (clim.netflux - clim.Ih)
    .rename("a")
    .hvplot.line(label="Jq0 - Ih", color="k", line_width=4)
).opts(show_grid=True, legend_position="right", frame_width=600, ylim=(0, 160))

Seasonal climatology, Vertical profile

def plot_seasonal_clim(ds, **kwargs):
    return (
        np.abs(
            ds.Jq.sel(time=slice("2005", "2011-02"))
            .sel(depth=slice(20, 70))
            # .mean("depth")
            .groupby("time.season")
            .mean()
            .sel(season=["DJF", "MAM", "JJA", "SON"])
        ).hvplot.line(flip_yaxis=True, y="depth", col="season", **kwargs)
        # .opts()
    )


h = (
    plot_seasonal_clim(mat, label=".mat")
    * plot_seasonal_clim(mat.where(mat.eps < 1e-4), label=".mat, ε < 1e-4")
    * plot_seasonal_clim(mat.where(mat.eps < 1e-5), label=".mat, ε < 1e-5")
    * plot_seasonal_clim(mat.where(mat.eps < 3e-6), label=".mat, ε < 3e-6")
    * plot_seasonal_clim(cchdo, label="CCHDO", color="k")
)
h.opts(plot_size=(270, 350), shared_xaxis=True, shared_yaxis=True, show_legend=True)

from cycler import cycler

with plt.rc_context({"axes.prop_cycle": cycler(color=["k", "r", "b", "g"])}):
    (
        (-1 * cchdo.Jq.where(cchdo.Jq > -1e3).squeeze())
        .sel(time=slice("2005", "2012-02-01"))
        .groupby("time.season")
        .mean()
        .sel(season=["DJF", "MAM", "JJA", "SON"])
        .cf.plot(y="depth", hue="season")
    )

Labeling ENSO phase transitions

pump.obs.process_oni().sel(time=slice("2005-Oct", "2016-Feb")).reset_coords(
    drop=True
).plot.line(aspect=3, size=5, color="k")
pump.obs.make_enso_transition_mask().sel(
    time=slice("2005-Oct", "2016-Feb")
).reset_coords(drop=True).plot.line(ax=plt.gca().twinx(), marker="x")
plt.grid(True, which="both", axis="both")

N2 vs N2T

Lot more data with N2T

tao_gridded.N2.cf.plot()

<matplotlib.collections.QuadMesh>

tao_gridded.N2T.cf.plot()

<matplotlib.collections.QuadMesh>

PDFs change

fg = tao_gridded.n2s2pdf.sel(
    enso_transition_phase=[
        "La-Nina cool",
        "La-Nina warm",
        "El-Nino warm",
        "El-Nino cool",
    ]
).plot(vmax=1, col="enso_transition_phase")
fg.set_titles("{value}")
fg.map(dcpy.plots.line45)
fg.axes[0, 0].set_xlim((-4.5, -2.5))
fg.axes[0, 0].set_ylim((-4.5, -2.5))

(-4.5, -2.5)

(
    tao_gridded.n2s2pdf.sel(
        enso_transition_phase=[
            "La-Nina cool",
            "La-Nina warm",
            "El-Nino warm",
            "El-Nino cool",
        ]
    )
    .sum("N2_bins")
    .plot(hue="enso_transition_phase")
)
plt.figure()
(
    tao_gridded.n2s2pdf.sel(
        enso_transition_phase=[
            "La-Nina cool",
            "La-Nina warm",
            "El-Nino warm",
            "El-Nino cool",
        ]
    )
    .sum("S2_bins")
    .plot(hue="enso_transition_phase")
)

[<matplotlib.lines.Line2D>,
 <matplotlib.lines.Line2D>,
 <matplotlib.lines.Line2D>,
 <matplotlib.lines.Line2D>]

fg = tao_gridded.n2s2Tpdf.sel(
    enso_transition_phase=[
        "La-Nina cool",
        "La-Nina warm",
        "El-Nino warm",
        "El-Nino cool",
    ]
).plot(vmax=1, col="enso_transition_phase")
fg.set_titles("{value}")
fg.map(dcpy.plots.line45)
fg.axes[0, 0].set_xlim((-4.5, -2.5))
fg.axes[0, 0].set_ylim((-4.5, -2.5))

(-4.5, -2.5)

(
    tao_gridded.n2s2Tpdf.sel(
        enso_transition_phase=[
            "La-Nina cool",
            "La-Nina warm",
            "El-Nino warm",
            "El-Nino cool",
        ]
    )
    .sum("N2_bins")
    .plot(hue="enso_transition_phase")
)
plt.figure()
(
    tao_gridded.n2s2Tpdf.sel(
        enso_transition_phase=[
            "La-Nina cool",
            "La-Nina warm",
            "El-Nino warm",
            "El-Nino cool",
        ]
    )
    .sum("S2_bins")
    .plot(hue="enso_transition_phase")
)

[<matplotlib.lines.Line2D>,
 <matplotlib.lines.Line2D>,
 <matplotlib.lines.Line2D>,
 <matplotlib.lines.Line2D>]